Weaning from ventilation using capnography

ABSTRACT

Devices and systems for monitoring weaning of a subject from a respiratory ventilator including a processing logic configured to characterize distinct patterns in a series of CO 2  waveforms, the distinct patterns indicative of the effectiveness of a weaning process; and to provide an indication relating to the effectiveness of the weaning process.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/188,069, filed Feb. 24, 2014 (published as US 2014/0171818 and nowU.S. Pat. No. 9,345,438), which is a continuation of U.S. applicationSer. No. 13/703,162, filed Dec. 10, 2012 (now U.S. Pat. No. 8,695,596),which is the U.S. National Stage of International Application No.PCT/IL2011/000453, filed Jun. 9, 2011, which claims the benefit of U.S.Provisional Application No. 61/353,243, filed Jun. 10, 2010, thecontents of each of which are herein expressly incorporated by referencefor all purposes.

FIELD OF THE INVENTION

Embodiments of the invention are related to the use of capnography inweaning a patient from a ventilator.

BACKGROUND

Mechanical ventilatory support is widely accepted as an effective formof therapy and means for treating patients with respiratory failure. Theearly generation of mechanical ventilators, prior to the mid-1960s, weredesigned to support alveolar ventilation and to provide supplementaloxygen for those patients who were unable to breathe on their own. Sincethat time, mechanical ventilators have become more sophisticated andcomplicated in response to increasing understanding of lungpathophysiology. For example, CPAP (Continuous Positive AirwayPressure), BiPAP (Bilevel Positive Airway Pressure) and SIMV(Synchronized Intermittent Mandatory Ventilation) are effective inpreventing need for intubation and also decreasing mortality in patientswith acute respiratory failure.

Even though the ventilator technology is constantly improving,ventilator dependence is still a serious medical and economic problem.It is well known that severe and sometimes lethal complications maydevelop the longer a patient is on ventilator support. In addition, asventilator therapy is provided in specialized and very expensiveIntensive Care Unit environments, there is a need to minimize ventilatordependency. Weaning the patients from artificial ventilation is amongthe most difficult challenges of the regarding intensive careventilation.

Prior art ventilator weaning monitoring methods are not efficient enoughand are often depending on subjective impressions of clinical fatigue ordistress and/or arterial blood gas derangements that of necessitymeasure failure after the failure has already developed. More modemmonitoring techniques such as the ratio of Tidal Volume to RespiratoryRate are still relatively crude indices of weaning performance. Weaningfrom ventilator dependency is potentially hazardous due to unexpectedprecipitous ventilatory failure, and early warning by appropriatemonitoring means is imperative for patient safety. Further, controlledstress of weakened respiratory muscles is imperative in order torecondition these muscles, but in addition to not over-stress theserecovering muscles and therefore cause further damage. It is oftendifficult to safely define the proper degree of weaning stressclinically.

There is thus a need in the art for systems, devices and methods thatwould allow monitoring the constantly changing condition of a patientundergoing weaning from ventilation.

The foregoing examples of the related art and limitations relatedtherewith are intended to be illustrative and not exclusive. Otherlimitations of the related art will become apparent to those of skill inthe art upon a reading of the specification and a study of the figures.

SUMMARY

The following embodiments and aspects thereof are described andillustrated in conjunction with systems, tools and methods which aremeant to be exemplary and illustrative, not limiting in scope.

In accordance with some aspects of the invention, systems, devices, andmethods are provided, which facilitate the monitoring the effectivenessand progression of a weaning process using data related to the level ofCO₂ in the expired breath of a ventilated patient.

In accordance with some embodiments of the invention, it wassurprisingly found that certain characteristics or combination ofcharacteristics relating to CO₂ waveform(s) of the expired breath of apatient are indicative to how well the weaning process is progressing.Characteristics related to CO₂ waveform(s) may include, for example, CO₂waveform characteristics, characteristics or behavior of groups of CO₂waveforms, time dependent behavior of the CO₂ waveforms, the frequencyof appearance of certain characteristics, the ratios between certaincharacteristics or any combination thereof.

In accordance with some embodiments of the invention, there is provideda method for monitoring a respiratory patient to evaluate respiratoryventilator weaning (and/or train the patient for respiratory ventilatorweaning), the method comprising characterizing one or more CO₂ waveformsobtained from expired breath of the respiratory patient (for example,detecting distinct patterns such as “sigh events”, “spike events” and“pools” among “regular” CO₂ waveforms, see FIG. 1-3 and the “DetailedDescription section) and evaluating respiratory ventilator weaning ofthe patient. The method may further include providing a signal to theventilator at least partially based on the characterized one or more CO₂waveforms and thus inducing a change of one or more parameters of theventilator (for example, changing the level of support to theventilation, allowing the patient to rest from the weaning attempts,changing the mode/program of weaning, etc.).

In accordance with some embodiments of the invention, there is provideda device for monitoring a respiratory patient to evaluate respiratoryventilator weaning (and/or train the patient for respiratory ventilatorweaning), the device comprising:

a capnograph configured to provide one or more CO₂ waveforms;

processing logic configured to: (a) characterize the one or more CO₂waveforms obtained by the capnograph (for example, detecting distinctpatterns in the CO₂ waveforms, such as, “sigh events”, “spike events”and “pools” among “regular” CO₂ waveforms, see FIG. 1-3 and the“Detailed Description section); and (b) provide a signal to theventilator at least partially based on the characterized one or more CO₂waveforms thereby induce a change of one or more parameters of theventilator (for example, changing the level of support to theventilation, allowing the patient to rest from the weaning attempts,changing the mode/program of weaning, etc.).

The prevalence and frequency of the waveforms characteristics, such asthe above mentioned events as an indicator may be different fordifferent ventilator modes. In other words, depending on the ventilatormode the event (for example, “sigh event”) frequency may have adifferent meaning. One could also perform coordinated changes in theventilator parameters/modes (for example, reductions of pressuresupport) and evaluate the consequent change in event pattern andaccordingly continue or stop the process.

The processing logic may be further configured to provide the signal tothe ventilator based on other parameters, in addition to thecharacteristics of the CO2 waveforms. Such parameters may includegeneral patient parameters, such as age, medical condition, medicalhistory, medications or any other parameter. The parameters may alsoinclude respiratory related parameters such as respiration rate, tidalvolumes, minute ventilation (the total lung ventilation per minute),inspiration rate and expiration rate at ambient pressure in the absenceof ventilator assistance, ventilator mode, ventilator setup parametersor any other parameter.

In accordance with some embodiments of the invention, there is provideda method for monitoring weaning of a subject from a respiratoryventilator, the method comprising characterizing distinct patterns in aseries of CO₂ waveforms obtained from expired breath of a subjectundergoing respiratory ventilation weaning, wherein said distinctpatterns are indicative to the effectiveness of a weaning process andwherein said distinct patterns are selected from a group consisting of“sigh events”, “spike events” and “pools” and providing an indicationrelating to the effectiveness of the weaning process.

The method may further include providing a signal to a ventilator atleast partially based on the characterized distinct patterns in theseries of CO₂ waveforms and thus inducing a change in one or moreparameters of the ventilator.

In accordance with some embodiments of the invention, there is provideda device for monitoring weaning of a subject from a respiratoryventilator, the device comprising: processing logic configured tocharacterize distinct patterns in the series of CO₂ waveforms, whereinsaid distinct patterns are indicative to the effectiveness of a weaningprocess and wherein said distinct patterns are selected from a groupconsisting of “sigh events”, “spike events” and “pools”, wherein saidprocessing logic is further configured to provide an indication relatingto the effectiveness of the weaning process.

In accordance with some embodiments of the invention, there is provideda system for monitoring weaning of a subject from a respiratoryventilator, the system comprising: a capnograph configured to provide aseries of CO₂ waveforms obtained from expired breath of a subjectundergoing respiratory ventilation weaning; and processing logicconfigured to characterize distinct patterns in the series of CO₂waveforms, wherein said distinct patterns are indicative to theeffectiveness of a weaning process and wherein said distinct patternsare selected from a group consisting of “sigh events”, “spike events”and “pools”, wherein said processing logic is further configured toprovide an indication relating to the effectiveness of the weaningprocess.

The change in one or more parameters of the ventilator may include achange in the level of support to the ventilation, allowing the patientto rest from weaning attempts, changing a mode/program of weaning or anycombination thereof.

Providing a signal to the ventilator may further be based on one or moreadditional parameters selected from the group consisting of: age of thesubject, medical condition of the subject, medical history of thesubject and medications administered to the subject.

Providing a signal to the ventilator may further be based on one or moreadditional parameters selected from the group consisting of: respirationrate, tidal volumes, minute ventilation (the total lung ventilation perminute), inspiration rate and expiration rate at ambient pressure in theabsence of ventilator assistance, ventilator mode and ventilator setupparameters.

Characterizing distinct patterns in a series of CO₂ waveforms mayinclude segmenting the series of CO₂ waveforms into breaths (waveforms),and calculating for each breath one or more features.

The one or more features ay include: area under the curve (AUC), breathduration (inhalation, expiration or both), I to E ratio, maximal CO₂value, minimal CO₂ value or any combination thereof.

Characterizing distinct patterns in the series of CO₂ waveforms mayinclude determining the extent and/or frequency of appearance of saiddistinct patterns. Characterizing distinct patterns in the series of CO₂waveforms may include determining the ratios between the distinctpatterns.

The processing logic may further be configured to provide a signal to aventilator at least partially based on the characterized distinctpatterns in the series of CO₂ waveforms and thus inducing a change inone or more parameters of the ventilator.

In addition to the exemplary aspects and embodiments described above,further aspects and embodiments will become apparent by reference to thefigures and by study of the following detailed description.

BRIEF DESCRIPTION OF THE FIGURES

Exemplary embodiments are illustrated in referenced figures. Dimensionsof components and features shown in the figures are generally chosen forconvenience and clarity of presentation and are not necessarily shown toscale. It is intended that the embodiments and figures disclosed hereinare to be considered illustrative rather than restrictive. The figuresare listed below.

FIGS. 1 A and B show series of CO₂ waveforms in a weaning patient,according to some embodiments of the invention;

FIG. 2 shows a series of CO₂ waveforms in a weaning patient, accordingto some embodiments of the invention;

FIGS. 3 A and B show series of CO₂ waveforms in a weaning patient,according to some embodiments of the invention;

FIG. 4 shows a series of CO₂ waveforms in different ventilation modes(CPAP, SIMV and VS), according to some embodiments of the invention;

FIG. 5 shows a “zoom-in” series of CO₂ waveforms taken from the CPAPsection of FIG. 4, according to some embodiments of the invention;

FIG. 6 shows a “zoom-in” series of CO₂ waveforms taken a few minutesafter starting the VS section of FIG. 4, according to some embodimentsof the invention; and

FIG. 7 shows a “zoom-in” series of CO₂ waveforms taken from the VSsection of FIG. 4, according to some embodiments of the invention.

DETAILED DESCRIPTION

In accordance with some embodiments of the invention, certain types ofdistinct breathing patterns (characteristics) in data obtained fromventilated patients during weaning process were found and evaluated.

According to some embodiments, the CO₂ signal obtained from expired airof a subject was segmented into breaths (waveforms), and one or morefeatures for each breath were calculated. Example of such featuresinclude area under the curve (AUC), breath duration (inhalation,expiration or both), I to E ratio (the ratio between inhalation toexpiration in a breath), maximal CO₂ value, minimal CO₂ value, presenceof small “dip(s)” in the waveform plateau or in the inhalation part ofthe breath (a dip in a waveform may be due to breathing effort that theventilation does not capture), and other features.

The extent and/or frequency of appearance of these features may, overtime, create certain patterns. According to some embodiments, thesepatterns may be indicative to the effectiveness of a weaning process.Three patterns were determined by their relative value of a feature orvalues of a set of features (as discussed herein according to someembodiments) compared to a pre-defined base-line or to an average ormedian feature value before weaning started (for example, when fullventilation was still applied) or to an average or median feature valueobtained during a specific time period (for example the last X hours,such as 2-10, 12 to 24). The following three patterns were determined:

1) Sigh Events: breaths with relatively large AUC (for example, comparedto a an average or median AUC value before weaning started or to theaverage or median AUC value obtained during a specific time period) orrelatively high breath duration (for example, compared to an average ormedian breath duration value before weaning started or to an average ormedian breath duration value obtained during a specific time period) orany other typical feature.

2) Spike Events: breaths with very small AUC (for example, compared to aan average or median AUC value before weaning started or to the averageor median AUC value obtained during a specific time period) orrelatively small breath duration and/or relatively low maximal CO₂ orany other typical feature (for example, compared to the respectivefeature value before weaning started or to an average or medianrespective feature value obtained during a specific time period).

3) Pools: group of breaths with relatively low AUC (for example,compared to a an average or median AUC value before weaning started orto the average or median AUC value obtained during a specific timeperiod) or low maximal CO₂ or low breath duration or any other typicalfeature (for example, compared to the respective feature value beforeweaning started or to an average or median respective feature valueobtained during a specific time period).

FIGS. 1-3 show series of CO₂ waveforms of weaning patients, according tosome embodiments of the invention. In each one of FIGS. 1-3 the distinctpatterns (FIG. 1: “sigh events”, FIG. 2. “spike events” and FIG. 3“pools”) are marked by arrows and can easily be observed among the“regular” breaths.

The patterns appear in different frequencies during weaning, atdifferent modes of ventilation (CPAP, SIMV and VS), as shown in FIG. 4.The arrows indicate the ventilation mode at that time.

FIG. 5 shows a “zoom-in” series of CO₂ waveforms taken from the CPAPsection of FIG. 4, according to some embodiments of the invention. Thedeterioration of the patient's condition with time, during CPAPventilation can clearly be seen by the higher frequency of “pools” (seearrow).

FIG. 6 shows a “zoom-in” series of CO₂ waveforms taken a few minutesafter starting the VS section of FIG. 4, according to some embodimentsof the invention. The “sigh events” are marked be arrows.

FIG. 7 shows a “zoom-in” series of CO₂ waveforms taken from the VSsection of FIG. 4, according to some embodiments of the invention. Thedeterioration of the patient's condition with time, during VS canclearly be seen by the higher frequency of “pools” (marked be horizontalarrows) in addition to the “sigh events” (marked be arrows).

It is also noted that the some of the characteristics (for example, thepooling effect) occurred together with changes in the Respiration Rate.

The appearance of the distinct patterns (for example, the “sigh events”,“spike events” and “pools”), their frequencies and extent, could serveas indicators for progress of the weaning process. Of course, the threedistinct patterns disclosed herein are merely examples and otherdistinct patterns (characteristics) that appear in the CO₂ waveforms orwaveforms series may also be indicative to the progress andeffectiveness of the weaning process.

According to some embodiments, the appearance of CO₂ distinct patternstheir frequencies and/or extent, optionally together with additionalventilator parameter (such as ventilation modes, tidal volume, minuteventilation, and PEEP (Positive End-Expiratory Pressure)) may serve asindicators to the progress and effectiveness of the weaning process.

The appearance of the CO₂ distinct patterns, their frequencies and/orextent, together with additional ventilator parameter (such asventilation modes, tidal volume, minute ventilation and PEEP) may alsoserve as indicators for ventilation related conditions such asair-leaks, a-synchrony, rebreathing, changes in compliance, obstructionand triggering effort.

Referring to the term “a-synchrony”: During weaning the patient breathspontaneous breaths and the ventilator “helps” by “filling in” andadding flow, for example, or in other ways (depending on the ventilatormode). A-synchrony is when the settings of the ventilator are such thatit interferes with the spontaneous breathing. For example, the patienttries to start a new breath in the middle of a ventilator breath.

Referring to the term “Rebreathing”: breathing a new breath withoutfinishing the previous breath.

What we claim is:
 1. A device for monitoring effectiveness of weaningsubject from a respiratory ventilator, the device comprising: amonitoring unit configured to measure and/or receive measurements of aCO₂ concentration over time in the subject's expired breath and toprovide a series of CO₂ waveforms based on the monitored concentration;a processor configured to: identify a distinct pattern in the series ofCO₂ waveforms; determine a frequency and/or an extent of the distinctpattern in the series of CO₂ waveforms; and provide an indicationrelating to the effectiveness of the weaning process, based on thedetermined frequency and/or extent of the distinct pattern; wherein anincrease in the frequency and/or the extent of the distinct pattern, ascompared to a baseline, is indicative of a deterioration in the weaningeffectiveness.
 2. The device of claim 1, wherein said distinct patternis selected from a group consisting of “sigh events”, “spike events” and“pools”.
 3. The device of claim 1, wherein said processor is furtherconfigured to determine one or more parameters of the ventilator basedon the determined frequency and/or extent of the distinct patterns inthe series of CO₂ waveforms.
 4. The device of claim 3, wherein the oneor more parameters of the ventilator comprises a change in the level ofsupport to the ventilation, allowing the patient to rest from weaningattempts, changing a mode/program of weaning or any combination thereof.5. The device of claim 3, wherein determining the one or more parameterof the ventilator is further based on one or more additional parametersselected from the group consisting of: age of the subject, medicalcondition of the subject, medical history of the subject and medicationsadministered to the subject.
 6. The device of claim 3, whereindetermining the one or more parameter of the ventilator is further basedon one or more additional parameters selected from the group consistingof: respiration rate, tidal volumes, minute ventilation (the total lungventilation per minute), inspiration rate and expiration rate at ambientpressure in the absence of ventilator assistance, ventilator mode andventilator setup parameters.
 7. The device of claim 1, whereincharacterizing distinct patterns in a series of CO₂ waveforms comprisessegmenting the series of CO₂ waveforms into breaths (waveforms), andcalculating for each breath one or more features.
 8. The device of claim7, wherein the one or more features comprise: area under the curve(AUC), breath duration (inhalation, expiration or both), I to E ratio,maximal CO₂ value, minimal CO₂ value or any combination thereof.
 9. Thedevice of claim 1, wherein characterizing distinct patterns in theseries of CO₂ waveforms comprises determining the ratios between thedistinct patterns.